The process of authenticating a physical object is commonly undertaken in many applications, such as conditional access to secure buildings or conditional access to digital data (e.g. stored in a computer or removable storage media), or for identification purposes (e.g. for charging an identified individual for a particular activity, or for boarding passengers at airports).
The use of biometrics for identification and/or authentication is to an ever increasing extent considered to be a better alternative to traditional identification means such as passwords, pin-codes and authentication tokens. In biometric identification, features that are unique to a user such as fingerprints, irises, shape of ears, facial appearance, etc. are used to provide identification of the user. Today, fingerprints are the most common biometric modality; roughly 70% of the biometric market uses fingerprints for identity verification. The majority of fingerprint algorithms are based on minutiae locations which are processed in an adequate manner to form a biometric template of an individual. These locations are estimated during enrolment and verification. During enrolment—i.e. the initial process when an enrolment authority acquires the biometric template of a user—the user offers her biometric to an enrolment device of the enrolment authority which generates and stores the template, possibly encrypted, in the system. During verification, the user again offers her biometric to the system, whereby the stored template is retrieved (and decrypted if required) and matching of the stored and a newly generated template is effected, i.e. the minutiae locations that were obtained during enrolment are compared to those acquired during verification. If there is a good enough match, the user is considered authenticated.
Alternative approaches use shape-related parameters, such as directional field of ridges and valleys of a fingerprint image. Such directional field is estimated as a function of the position in the fingerprint and subsequently used as feature data (or a derivative thereof). Translations and rotations of the measurement data cause major problems when minutiae locations or shape-related parameters are to be matched. A user may place her finger differently during verification than during enrolment. In most cases, the comparison stage during verification requires a template alignment step before the actual comparison process is employed, in order to compensate for translation and rotation differences. More advanced comparison algorithms also take non-linear distortions into account.
In order to safeguard the integrity of individuals employing a biometric identification system whenever a breach of secrecy occurs in the system, cryptographic techniques to encrypt or hash the biometric templates and perform the verification (or matching) on the encrypted data such that the real template is never available in the clear can be envisaged. With the advent of these template protection techniques, which employ encryption or one-way functions to biometric data, template alignment during comparison is virtually impossible. Comparison is employed in the encrypted domain, and hence there is no access to the original biometric data for alignment or analysis purposes. As a result, alignment issues have to be resolved as a pre-processing step before generating the template.
A known method for alignment as a pre-processing step is to extract features, and to correct minutiae data by means of a certain reference point. This reference point could be found and/or generated with the help of core location(s), delta location, the average minutiae location, or any other relatively stable, reproducible reference location within the fingerprint image. If features are defined relative to this reference point, and this process is defined similarly for enrolment and verification, there is no need for an additional alignment step during comparison in the verification phase. Although the method of employing a reference point is quite successful in many cases, it can also cause problems. From empirical tests, it is estimated that for state-of-the-art fingerprint analysis algorithms, about 10% of the fingerprints do not have a reliable reference point (e.g., a unique core location). In such cases, alignment is performed using a badly estimated reference point or is not possible at all. In the verification phase, this has the consequence that an individual very well may be rejected even though she in fact should be authorized, which results in a significant degradation of verification performance on average for a whole population. Clearly, it is desirable not to erroneously reject authorized individuals, i.e. a low false rejection rate (FRR) is required. Thus, individuals having biometric characteristics not suitable for extraction of a reference point will either experience enrolment failure or a high FRR in the verification phase.